AutoML Leaderboard
| Best model | name | model_type | metric_type | metric_value | train_time | single_prediction_time |
|---|---|---|---|---|---|---|
| 1_Default_LightGBM | LightGBM | rmse | 170.896 | 15.61 | 0.0141 | |
| 2_Default_Xgboost | Xgboost | rmse | 170.468 | 5.64 | 0.0112 | |
| 3_Default_CatBoost | CatBoost | rmse | 169.979 | 4.29 | 0.0199 | |
| 4_Default_NeuralNetwork | Neural Network | rmse | 170.801 | 8.31 | 0.0181 | |
| 5_Default_RandomForest | Random Forest | rmse | 172.298 | 7.4 | 0.0865 | |
| 10_LightGBM | LightGBM | rmse | 172.388 | 4.74 | 0.0089 | |
| 6_Xgboost | Xgboost | rmse | 171.529 | 2.83 | 0.0099 | |
| 14_CatBoost | CatBoost | rmse | 169.973 | 3.38 | 0.0086 | |
| 18_RandomForest | Random Forest | rmse | 173.056 | 4.53 | 0.0856 | |
| 22_NeuralNetwork | Neural Network | rmse | 171.033 | 3.66 | 0.0147 | |
| 11_LightGBM | LightGBM | rmse | 170.98 | 4.52 | 0.0088 | |
| 7_Xgboost | Xgboost | rmse | 171.658 | 2.84 | 0.0106 | |
| 15_CatBoost | CatBoost | rmse | 170.012 | 3.17 | 0.0093 | |
| 19_RandomForest | Random Forest | rmse | 175.2 | 6.63 | 0.0879 | |
| 23_NeuralNetwork | Neural Network | rmse | 170.741 | 9.64 | 0.0147 | |
| 12_LightGBM | LightGBM | rmse | 170.878 | 6.03 | 0.0091 | |
| 8_Xgboost | Xgboost | rmse | 173.356 | 5.22 | 0.0102 | |
| 16_CatBoost | CatBoost | rmse | 170.001 | 2.78 | 0.0085 | |
| 20_RandomForest | Random Forest | rmse | 170.212 | 7.36 | 0.081 | |
| 24_NeuralNetwork | Neural Network | rmse | 171.3 | 7.69 | 0.0148 | |
| 13_LightGBM | LightGBM | rmse | 171.342 | 7.36 | 0.0087 | |
| 9_Xgboost | Xgboost | rmse | 173.221 | 4.41 | 0.011 | |
| 17_CatBoost | CatBoost | rmse | 170.035 | 3.01 | 0.0098 | |
| 21_RandomForest | Random Forest | rmse | 170.759 | 7.37 | 0.0881 | |
| 25_NeuralNetwork | Neural Network | rmse | 171.258 | 5.92 | 0.0146 | |
| 14_CatBoost_GoldenFeatures | CatBoost | rmse | 169.999 | 4.99 | 0.0201 | |
| 3_Default_CatBoost_GoldenFeatures | CatBoost | rmse | 170.056 | 3.26 | 0.0193 | |
| 16_CatBoost_GoldenFeatures | CatBoost | rmse | 170.086 | 3.59 | 0.0165 | |
| 14_CatBoost_RandomFeature | CatBoost | rmse | 170.043 | 4.17 | 0.0111 | |
| 26_CatBoost | CatBoost | rmse | 169.967 | 4.77 | 0.0086 | |
| 27_CatBoost | CatBoost | rmse | 170.036 | 3.08 | 0.0086 | |
| 28_CatBoost | CatBoost | rmse | 169.973 | 4.24 | 0.04 | |
| 29_CatBoost | CatBoost | rmse | 170.084 | 3.16 | 0.0101 | |
| 30_RandomForest | Random Forest | rmse | 170.698 | 7.08 | 0.0783 | |
| 31_RandomForest | Random Forest | rmse | 170.286 | 8.68 | 0.0787 | |
| 32_Xgboost | Xgboost | rmse | 170.216 | 2.88 | 0.0104 | |
| 33_Xgboost | Xgboost | rmse | 171.038 | 3.5 | 0.0103 | |
| 34_NeuralNetwork | Neural Network | rmse | 170.513 | 5.88 | 0.0147 | |
| 35_RandomForest | Random Forest | rmse | 171.47 | 4.31 | 0.0787 | |
| 36_RandomForest | Random Forest | rmse | 170.389 | 8.48 | 0.0821 | |
| 37_NeuralNetwork | Neural Network | rmse | 170.833 | 4.91 | 0.0147 | |
| 38_NeuralNetwork | Neural Network | rmse | 171.007 | 6.74 | 0.0148 | |
| 39_LightGBM | LightGBM | rmse | 170.904 | 4.68 | 0.0087 | |
| 40_LightGBM | LightGBM | rmse | 170.879 | 4.88 | 0.011 | |
| 41_Xgboost | Xgboost | rmse | 170.908 | 3.15 | 0.0106 | |
| 42_Xgboost | Xgboost | rmse | 172.349 | 3.6 | 0.0107 | |
| 43_CatBoost | CatBoost | rmse | 169.976 | 4.74 | 0.0086 | |
| 44_CatBoost | CatBoost | rmse | 169.947 | 5.98 | 0.0085 | |
| 45_CatBoost | CatBoost | rmse | 169.972 | 4.1 | 0.0094 | |
| 46_CatBoost | CatBoost | rmse | 169.981 | 4.02 | 0.0085 | |
| 47_RandomForest | Random Forest | rmse | 170.328 | 7.12 | 0.099 | |
| 48_Xgboost | Xgboost | rmse | 170.246 | 3.36 | 0.0106 | |
| 49_Xgboost | Xgboost | rmse | 170.262 | 2.95 | 0.0099 | |
| 50_RandomForest | Random Forest | rmse | 170.199 | 7.66 | 0.0874 | |
| 51_Xgboost | Xgboost | rmse | 170.467 | 4.28 | 0.0407 | |
| 52_Xgboost | Xgboost | rmse | 170.445 | 8.25 | 0.0102 | |
| 53_NeuralNetwork | Neural Network | rmse | 170.454 | 6.28 | 0.0284 | |
| 54_NeuralNetwork | Neural Network | rmse | 170.611 | 6.11 | 0.0173 | |
| 55_NeuralNetwork | Neural Network | rmse | 170.515 | 9.52 | 0.0171 | |
| 56_NeuralNetwork | Neural Network | rmse | 170.386 | 10.3 | 0.0155 | |
| 57_LightGBM | LightGBM | rmse | 170.387 | 4.88 | 0.0088 | |
| 58_LightGBM | LightGBM | rmse | 171.3 | 7.61 | 0.0154 | |
| 59_LightGBM | LightGBM | rmse | 170.365 | 5.21 | 0.0088 | |
| 60_LightGBM | LightGBM | rmse | 171.297 | 7.24 | 0.0087 | |
| the best | Ensemble | Ensemble | rmse | 169.734 | 2.84 | 0.125 |
AutoML Performance
AutoML Performance Boxplot
Features Importance (Original Scale)
Scaled Features Importance (MinMax per Model)
Spearman Correlation of Models
Summary of 4_Default_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.784 |
| MSE | 29172.8 |
| RMSE | 170.801 |
| R2 | 0.620462 |
| MAPE | 0.19442 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 29_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.162 |
| MSE | 28928.5 |
| RMSE | 170.084 |
| R2 | 0.62364 |
| MAPE | 0.193586 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 28_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.026 |
| MSE | 28890.7 |
| RMSE | 169.973 |
| R2 | 0.624131 |
| MAPE | 0.193452 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 17_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.2
- depth: 8
- rsm: 1.0
- loss_function: MAE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.113 |
| MSE | 28912 |
| RMSE | 170.035 |
| R2 | 0.623854 |
| MAPE | 0.193996 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 16_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.101 |
| MSE | 28900.4 |
| RMSE | 170.001 |
| R2 | 0.624006 |
| MAPE | 0.19347 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of Ensemble
Ensemble structure
| Model | Weight |
|---|---|
| 11_LightGBM | 12 |
| 14_CatBoost_GoldenFeatures | 6 |
| 15_CatBoost | 6 |
| 17_CatBoost | 16 |
| 37_NeuralNetwork | 1 |
| 3_Default_CatBoost | 1 |
| 44_CatBoost | 1 |
| 4_Default_NeuralNetwork | 2 |
| 56_NeuralNetwork | 7 |
| 9_Xgboost | 2 |
Metric details:
| Metric | Score |
|---|---|
| MAE | 131.866 |
| MSE | 28809.7 |
| RMSE | 169.734 |
| R2 | 0.625186 |
| MAPE | 0.193671 |
Learning curves
True vs Predicted
Predicted vs Residuals
Summary of 43_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.025
- depth: 7
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.047 |
| MSE | 28891.8 |
| RMSE | 169.976 |
| R2 | 0.624118 |
| MAPE | 0.193461 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 38_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.993 |
| MSE | 29243.3 |
| RMSE | 171.007 |
| R2 | 0.619545 |
| MAPE | 0.194496 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 60_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 95
- learning_rate: 0.1
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.175 |
| MSE | 29342.6 |
| RMSE | 171.297 |
| R2 | 0.618253 |
| MAPE | 0.195212 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 25_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 32
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.171 |
| MSE | 29329.3 |
| RMSE | 171.258 |
| R2 | 0.618426 |
| MAPE | 0.194694 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 50_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 40
- max_depth: 7
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.33 |
| MSE | 28967.7 |
| RMSE | 170.199 |
| R2 | 0.62313 |
| MAPE | 0.193704 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 58_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 95
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.146 |
| MSE | 29343.8 |
| RMSE | 171.3 |
| R2 | 0.618237 |
| MAPE | 0.195187 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 59_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 31
- learning_rate: 0.1
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.396 |
| MSE | 29024.2 |
| RMSE | 170.365 |
| R2 | 0.622395 |
| MAPE | 0.193989 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 55_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 8
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.546 |
| MSE | 29075.5 |
| RMSE | 170.515 |
| R2 | 0.621728 |
| MAPE | 0.194036 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 11_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.2
- feature_fraction: 0.5
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.859 |
| MSE | 29234.3 |
| RMSE | 170.98 |
| R2 | 0.619662 |
| MAPE | 0.194725 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 10_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 15
- learning_rate: 0.05
- feature_fraction: 0.8
- bagging_fraction: 0.5
- min_data_in_leaf: 50
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 134.194 |
| MSE | 29717.5 |
| RMSE | 172.388 |
| R2 | 0.613375 |
| MAPE | 0.196031 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 3_Default_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.054 |
| MSE | 28892.7 |
| RMSE | 169.979 |
| R2 | 0.624106 |
| MAPE | 0.19349 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 8_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 7
- min_child_weight: 25
- subsample: 0.9
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 135.13 |
| MSE | 30052.3 |
| RMSE | 173.356 |
| R2 | 0.60902 |
| MAPE | 0.197176 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 44_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.025
- depth: 9
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.038 |
| MSE | 28882.1 |
| RMSE | 169.947 |
| R2 | 0.624244 |
| MAPE | 0.193493 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 45_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 5
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.056 |
| MSE | 28890.4 |
| RMSE | 169.972 |
| R2 | 0.624136 |
| MAPE | 0.193432 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 21_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.6
- min_samples_split: 50
- max_depth: 6
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.904 |
| MSE | 29158.6 |
| RMSE | 170.759 |
| R2 | 0.620646 |
| MAPE | 0.194606 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 56_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 32
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
10.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.238 |
| MSE | 29031.4 |
| RMSE | 170.386 |
| R2 | 0.622302 |
| MAPE | 0.19419 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 14_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.108 |
| MSE | 28899.8 |
| RMSE | 169.999 |
| R2 | 0.624014 |
| MAPE | 0.193802 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 9_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.15
- max_depth: 8
- min_child_weight: 50
- subsample: 0.6
- colsample_bytree: 0.6
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 135.066 |
| MSE | 30005.4 |
| RMSE | 173.221 |
| R2 | 0.60963 |
| MAPE | 0.197142 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 20_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 1.0
- min_samples_split: 40
- max_depth: 6
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.351 |
| MSE | 28972 |
| RMSE | 170.212 |
| R2 | 0.623074 |
| MAPE | 0.193674 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 22_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 4
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.075 |
| MSE | 29252.1 |
| RMSE | 171.033 |
| R2 | 0.61943 |
| MAPE | 0.193167 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 35_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.6
- min_samples_split: 50
- max_depth: 5
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.651 |
| MSE | 29402.1 |
| RMSE | 171.47 |
| R2 | 0.617479 |
| MAPE | 0.195119 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 39_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.1
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.839 |
| MSE | 29208.3 |
| RMSE | 170.904 |
| R2 | 0.62 |
| MAPE | 0.194633 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 53_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 8
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.426 |
| MSE | 29054.4 |
| RMSE | 170.454 |
| R2 | 0.622002 |
| MAPE | 0.193308 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 34_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.493 |
| MSE | 29074.7 |
| RMSE | 170.513 |
| R2 | 0.621738 |
| MAPE | 0.194056 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 57_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 31
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.397 |
| MSE | 29031.6 |
| RMSE | 170.387 |
| R2 | 0.622299 |
| MAPE | 0.194034 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 23_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 16
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
9.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.762 |
| MSE | 29152.6 |
| RMSE | 170.741 |
| R2 | 0.620725 |
| MAPE | 0.19466 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 52_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.481 |
| MSE | 29051.4 |
| RMSE | 170.445 |
| R2 | 0.622041 |
| MAPE | 0.19421 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 42_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 9
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 134.041 |
| MSE | 29704.2 |
| RMSE | 172.349 |
| R2 | 0.613549 |
| MAPE | 0.196358 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 30_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 1.0
- min_samples_split: 40
- max_depth: 5
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.8 |
| MSE | 29137.9 |
| RMSE | 170.698 |
| R2 | 0.620915 |
| MAPE | 0.193984 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 47_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 40
- max_depth: 6
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.509 |
| MSE | 29011.7 |
| RMSE | 170.328 |
| R2 | 0.622558 |
| MAPE | 0.1938 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 37_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 16
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.881 |
| MSE | 29184.1 |
| RMSE | 170.833 |
| R2 | 0.620315 |
| MAPE | 0.194431 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 16_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 7
- rsm: 1.0
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.243 |
| MSE | 28929.4 |
| RMSE | 170.086 |
| R2 | 0.623629 |
| MAPE | 0.193876 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 24_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 64
- dense_2_size: 32
- learning_rate: 0.08
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.965 |
| MSE | 29343.6 |
| RMSE | 171.3 |
| R2 | 0.61824 |
| MAPE | 0.195578 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 3_Default_CatBoost_GoldenFeatures
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 6
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.136 |
| MSE | 28919.1 |
| RMSE | 170.056 |
| R2 | 0.623763 |
| MAPE | 0.193704 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 40_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.1
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.874 |
| MSE | 29199.7 |
| RMSE | 170.879 |
| R2 | 0.620112 |
| MAPE | 0.194712 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 31_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 1.0
- min_samples_split: 40
- max_depth: 7
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.4 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.331 |
| MSE | 28997.4 |
| RMSE | 170.286 |
| R2 | 0.622744 |
| MAPE | 0.19374 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 36_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.6
- min_samples_split: 50
- max_depth: 7
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
8.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.544 |
| MSE | 29032.4 |
| RMSE | 170.389 |
| R2 | 0.622288 |
| MAPE | 0.194272 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 2_Default_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.51 |
| MSE | 29059.5 |
| RMSE | 170.468 |
| R2 | 0.621936 |
| MAPE | 0.194258 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 14_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.021 |
| MSE | 28890.9 |
| RMSE | 169.973 |
| R2 | 0.62413 |
| MAPE | 0.193452 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 15_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 1.0
- loss_function: MAE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.113 |
| MSE | 28904.2 |
| RMSE | 170.012 |
| R2 | 0.623957 |
| MAPE | 0.194149 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 54_NeuralNetwork
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 32
- learning_rate: 0.01
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.726 |
| MSE | 29108.3 |
| RMSE | 170.611 |
| R2 | 0.621302 |
| MAPE | 0.19435 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 51_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.05
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.0 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.488 |
| MSE | 29059.1 |
| RMSE | 170.467 |
| R2 | 0.621941 |
| MAPE | 0.194198 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 41_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 7
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.911 |
| MSE | 29209.6 |
| RMSE | 170.908 |
| R2 | 0.619984 |
| MAPE | 0.194859 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 27_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.1
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.125 |
| MSE | 28912.2 |
| RMSE | 170.036 |
| R2 | 0.623853 |
| MAPE | 0.193657 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 26_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.025
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.5 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.052 |
| MSE | 28888.8 |
| RMSE | 169.967 |
| R2 | 0.624157 |
| MAPE | 0.193617 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 33_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 7
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.2 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.981 |
| MSE | 29254 |
| RMSE | 171.038 |
| R2 | 0.619406 |
| MAPE | 0.194887 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 48_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.05
- max_depth: 5
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.296 |
| MSE | 28983.7 |
| RMSE | 170.246 |
| R2 | 0.622923 |
| MAPE | 0.193946 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 6_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 8
- min_child_weight: 5
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.423 |
| MSE | 29422.3 |
| RMSE | 171.529 |
| R2 | 0.617216 |
| MAPE | 0.195553 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 18_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.5
- min_samples_split: 20
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
4.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 135.04 |
| MSE | 29948.5 |
| RMSE | 173.056 |
| R2 | 0.61037 |
| MAPE | 0.196956 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 12_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 1.0
- min_data_in_leaf: 20
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
5.8 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.78 |
| MSE | 29199.1 |
| RMSE | 170.878 |
| R2 | 0.620119 |
| MAPE | 0.194604 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 13_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 95
- learning_rate: 0.05
- feature_fraction: 1.0
- bagging_fraction: 1.0
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.233 |
| MSE | 29358.2 |
| RMSE | 171.342 |
| R2 | 0.61805 |
| MAPE | 0.195216 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 49_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 5
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.287 |
| MSE | 28989.1 |
| RMSE | 170.262 |
| R2 | 0.622852 |
| MAPE | 0.1939 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 5_Default_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
7.1 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 134.307 |
| MSE | 29686.6 |
| RMSE | 172.298 |
| R2 | 0.613777 |
| MAPE | 0.195516 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 46_CatBoost
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 7
- rsm: 1
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.7 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.054 |
| MSE | 28893.6 |
| RMSE | 169.981 |
| R2 | 0.624094 |
| MAPE | 0.193451 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 7_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.1
- max_depth: 8
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 133.505 |
| MSE | 29466.3 |
| RMSE | 171.658 |
| R2 | 0.616643 |
| MAPE | 0.195706 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 1_Default_LightGBM
LightGBM
- n_jobs: -1
- objective: regression
- num_leaves: 63
- learning_rate: 0.05
- feature_fraction: 0.9
- bagging_fraction: 0.9
- min_data_in_leaf: 10
- metric: rmse
- custom_eval_metric_name: None
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
15.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.86 |
| MSE | 29205.6 |
| RMSE | 170.896 |
| R2 | 0.620035 |
| MAPE | 0.194671 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 32_Xgboost
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: reg:squarederror
- eta: 0.075
- max_depth: 5
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
2.6 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.299 |
| MSE | 28973.4 |
| RMSE | 170.216 |
| R2 | 0.623056 |
| MAPE | 0.19394 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 19_RandomForest
Random Forest
- n_jobs: -1
- criterion: squared_error
- max_features: 0.7
- min_samples_split: 50
- max_depth: 3
- eval_metric_name: rmse
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
6.3 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 136.858 |
| MSE | 30695.1 |
| RMSE | 175.2 |
| R2 | 0.600656 |
| MAPE | 0.197695 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
Summary of 14_CatBoost_RandomFeature
CatBoost
- n_jobs: -1
- learning_rate: 0.05
- depth: 8
- rsm: 0.8
- loss_function: RMSE
- eval_metric: RMSE
- explain_level: 1
Validation
- validation_type: kfold
- k_folds: 5
- shuffle: True
Optimized metric
rmse
Training time
3.9 seconds
Metric details:
| Metric | Score |
|---|---|
| MAE | 132.174 |
| MSE | 28914.7 |
| RMSE | 170.043 |
| R2 | 0.623819 |
| MAPE | 0.193379 |
Learning curves
Permutation-based Importance
True vs Predicted
Predicted vs Residuals
